In Table. REF , we evaluate the robustness of AD methods with the additional noisy features.
More specifically, we follow the experiment settings in [1]} to include 50 additional noisy features which are randomly sampled from \([-1, 1]\)  to Thyroid and Mammography datasets, and create Thyroid (noise) and Mammography (noise) respectively.
In Table. REF , we show that the performance of DIAD is robust with additional noisy features (76.1\(\rightarrow \) 71.1, 85.0\(\rightarrow \) 83.1), while others show significant performance degradation. For example, on Thyroid (noise), ICL decreases from 75.9\(\rightarrow \) 49.5, KNN from 75.1\(\rightarrow \) 49.5, and COPOD from 77.6\(\rightarrow \) 60.5.
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